What really happens to small and medium-sized enterprises in a global
economic recession? UK evidence on sales and job dynamics
Marc Cowling
Weixi Liu
Exeter Business School
Andrew Ledger
Department for Business Innovation and Skills
Keywords: smaller firms; job dynamics; sales dynamics; financial crisis
Abstract
We use UK data to consider how small and medium enterprises coped during the financial
crisis. This is important as SMEs are major contributors to job creation, but are vulnerable to
falling demand. We find that 4 in 10 SMEs experienced a fall in employment during the
recession, and 5 in 10 a fall in sales. Within 12 months of the recession, three-quarters of
entrepreneurs had a desire to grow. This suggests that whilst the immediate effects of
recession are severe, entrepreneurs recover quite quickly. Importantly, recessionary growth is
hugely concentrated amongst entrepreneurs with the highest human capital.
1.
Introduction
“There has been limited attention from the academic community in examining its [the Great
Recession of 2008] effect on entrepreneurial activity and the sustainability of the small
business sector” Saradakis (2012: p.733)
The financial crisis, which began in September 2008, contributed to a fall of 6.4% in UK
GDP in the subsequent six quarters that constituted the official recession. This represents the
loss of three years of trend level economic growth for the UK economy. At a time when
larger businesses shed vast numbers of employees, and general unemployment rose by
674,000, policy-makers increasingly looked to small and medium enterprises (SMEs) to
provide new employment opportunities and help drag the economy out of recession
(Department for Business, Innovation and Skills, 2013). The implicit assumption being that
(a) SMEs are more flexible and resilient (Smallbone, et al., 2012; Bednarzik, 2000; Binks and
Jennings, 1986), and, (b) SMEs are more labour intensive (Cowling, 2003; Robbins, et al.,
2000), and, (c) that periods of disequilibrium create new opportunities for entrepreneurs
(Schumpeter, 1942; Parker, et al, 2012).
Yet even if we generally believe that the small business sector of the economy is more
dynamic and opportunistic than the large firms sector, SMEs are not immune to large
contractions in the general demand for goods and services. But within the small business
sector there is evidence that periods of disequilibrium and economic instability are precisely
the times when the best entrepreneurs are able to take advantage of new opportunities as large
firms and the public sector withdraw from markets (Acs and Storey, 2004; Grilli, 2010). This
is an entrepreneurial quality effect, in effect separating the wheat from the chaff (Kitson,
1995). This occurs as in periods of economic growth more people become willing to pursue
an entrepreneurial career path, but the marginal quality of the last entrepreneur declines. In
recessions, low quality, marginal, entrepreneurs exit the market.
It is the intention of this paper to use a unique longitudinal data set for the UK, which
spans the period leading up to the financial crisis in September 2008 and all through the
subsequent recession, to address 4 key questions;

How many SMEs have still managed to grow in the current recession?

Has the small business sector being able to maintain its employment levels during the
current recession?

What types of entrepreneurs and SMEs have shown the capability to grow and create
jobs during the current recession (is there an entrepreneurial human capital (EHC)
effect)?

Can SMEs provide the future growth that will create new employment opportunities
as the economy emerges from recession?
In doing so, we hope to add to our general understanding of what really happens to the
smaller business sector during a severe economic downturn. This will enable us to speculate
about the potential contribution of the small business sector to future economic growth. This
is of great importance given the political onus placed on the small business sector to provide
new jobs and economic prosperity in the future. Our results also make a contribution to the
future theoretical development of entrepreneurial growth models in periods of economic
disequilibrium and turbulence.
The value added of our paper is fourfold. Firstly, we use a unique and up to the minute data
set covering a full, and severe, economic recession cycle. Secondly, we have multiple
measures of actual growth and an additional future growth orientation variable. Previous
empirical studies of growth tended to use single performance measures, with Delmar (1997),
in an analysis of 55 growth studies, finding only 18.2% used more than one measure, and
Unger et al (2011), in a recent analysis of 70 growth studies, finding that the use of multiple
growth measures had only marginally increased to 21.4%. Thirdly, our data set contains a
rich set of entrepreneur and business level characteristics which allows us to broaden the
theoretical and empirical scope of our analysis. Fourthly, we are able to explicitly test
whether general relationships (for example between entrepreneurial human capital, EHC, and
growth) hold during a severe recessionary environment or whether these relationships lose
their effect.
The rest of the paper is organised as follows. In the next section we review some of the key
literature relating to the measurement of growth and its determinants. We also formulate our
hypotheses. Section 3 presents out data and discusses key variables to be used in our analysis.
Section 4 presents the results of our empirical analyses. Section 5 explores the significance
and relevance of the results of our study and draw out the implications for policy-makers and
practitioners. The last section concludes the paper.
2.
Small Business Growth: Measurements and Determinants
The growth literature has put too little emphasis on the measurement of growth (Delmar,
1997). Only recently has growth started to be treat as a multidimensional, heterogeneous and
complex construct (Achtenhagen et al., 2011; Leitch et al., 2010). This study uses multiple
indicators to measure small business growth, namely changes in employment and sales. The
reasons for choosing employment and sales as growth measures are three-fold. First, it is
widely argued that small businesses make a positive contribution to economies mainly
through employment and productivity (Acs and Storey, 2004; Audretsch et al, 2008; Cowling,
2006), making employment and sales two natural candidates and mostly used variables for
growth measures (Achtenhagen et al, 2011; Delmar, 1997; Unger et al, 2011; Weinzimmer et
al, 1998). Second, recent reviews of small business growth literature found that previous
studies tended to use single performance measures and this approach often leads to results
that are not comparable with each other (Achtenhagen et al, 2010; Delmar, 1997;
Weinzimmer et al, 1998). Delmar (1997) suggests the use of multiple growth measures as
they might “best represent the theoretical concept of growth (p. 203)”. Third, as suggested in
Achtenhagen et al (2010), current entrepreneurship studies tend to ‘simplify’ growth
outcomes to easily observable measures such as employee numbers, and ignore ‘the
multidimensionality and complexity of growth processes’, thus creating a gap between the
growth defined and measured by academics or policy makers, and what is meaningful and
relevant to entrepreneurs. This appears not to be so much of a concern in our study as when
asked the question in the Annual Small Business Survey, on which this research is based,
more than 4 out of 5 entrepreneurs regard increasing turnover as means to achieve their
longer term growth plans. This ensures that the practical and policy implications derived from
our empirical analyses are meaningful and relevant to practitioners and policy makers.
After justifying our choice of growth measures, we then draw on studies that have adopted
a multivariate approach to examining the determinants of growth, from which we develop our
main hypotheses to be tested using multivariate regression analysis. Compared to large firms,
small businesses often lack the relevant resources and network capabilities to achieve growth
(Storey, 1994). Facing this greater uncertainty toward the external environment than large
firms, SMEs have a higher tendency to innovate products and services in order to sustain
continuous evolution and change (Garengo et al, 2005). Therefore, the ability to undertake
entrepreneurial activities, or the level of entrepreneurship, is critical for small businesses’
survival, growth and success. In this study, the level of entrepreneurship is linked with four
broader categories of variables: business characteristics, entrepreneur characteristics and
human capital, growth orientation and access to finance. However as found in Cassar (2007),
the achieved venture growth by SMEs can also vary due to heterogeneous career reasons and
growth preferences of entrepreneurs, which are two areas not pursued in this study.
2.1.
Business characteristics
Industry sector, age and size are three of the most common business characteristics to be
linked with small business growth. Regarding industry, we might expect to observe an
empirical relationship including economies of scale, barriers to growth, competition, overall
market growth etc. In line with our a priori thinking, we note that in a majority of studies that
have tested for any such effects a significant industry effect is apparent. The most common
sectors associated with higher growth rates are businesses services and manufacturing. And
those associated with lower growth rates are personal household and other services.
Reassuringly, this result holds across countries (see Durand and Coeurderoy, 2001, for
French evidence, Cooper et al, 1994, for US evidence and, Meager et al, 2004, for UK
evidence).
The age of businesses can also have an effect on realised growth. Literature on small
business survival suggests that younger businesses in their formative years are more likely to
be concerned with survival than growth if they do not fail within the first few years of
starting up (Cowling, 2006). Therefore, growth should be observed in more matured
businesses which have passed the ‘survival mode’ (Audrestch and Mahmood 1994; Watson,
2012). On the other hand, older firms may also suffer from the owners’ lower commitment
and involvement compared to young firms (Churchill and Lewis, 1983), so a firm’s
performance is usually found to be diminishing as the firm ages (Chandler and Hanks, 1993
and 1994; Durand and Coeurderoy, 2001; Nunes, 2013).
Business size at start-up is also an important variable included in a number of empirical
studies. Although the famous Gibrat’s law (Gibrat, 1931) suggests no relation between size
and growth, in the small business sector we might predict that size is an indicator of resource
availability, both in financial and human capital terms, and in particular quality of the
entrepreneur or entrepreneurial team. Also, bigger firms may enjoy greater economies of
scale, compared with smaller firms (Dass, 2000). As such size should be associated with
higher growth rates, which is confirmed by some empirical studies (e.g., Cowling et al, 2008;
Sapienza and Grimm, 1997; Zhao et al, 2011). However, there is a trade-off between firm
size and efficiency (Dean et al, 1998), which ultimately influences the firm’s performance.
According to this trade-off theory, small firms may have a tendency to remain small
(Heshmati, 2000; Power and Reid, 2003).
From our general review of the literature it is clear that business characteristics play a
significant part in determining the rate of growth of firms. In a recessionary economic
environment we predict that these effects will maintain, or even become more important in
terms of magnitude and their ability to distinguish between growing, stable and declining
firms. This might occur as external resources become scarcer during recessions so firms are
forced to rely on internal resources and strategic reserves.
H1: Business characteristics (age, size, sector, etc.) will impact on the rate of small business
growth in both recessionary and non-recessionary periods.
2.2.
Entrepreneur characteristics and human capital
Entrepreneurs’ personal characteristics and their potential impact on small business growth
performance are important considerations for both scholars and policy makers especially
when there is perceived discrimination against certain groups of entrepreneurs such as
women or ethnic minorities.
Perhaps the most interesting feature to note is that relatively few studies actually test for
these effects. And the vast majority of studies that do are European. On gender, for example,
only the US based studies of Sapienza et al (1997), which reports no gender effect, and
Cooper et al (1994), which finds a positive effect for males, explore the gender issue. In
European studies, Cowling (2002) in an EU wide study finds a positive effect for males,
which is in line with the Bosma et al (2002) Netherlands study, the Bruderl and Preisendorfer
(1998) German study. Only Cowling (2003) finds a positive female effect for those using a
publicly funded business start-up programme in deprived areas of England.
Concerning other personal characteristics, the empirical evidence is significantly less
voluminous. On ethnicity, for example, only Cooper et al (1994) for the US and Cowling
(2003) for deprived areas of England, find any ethnicity impacts. In both cases they identified
a positive effect for white people. This contrasts with the UK based study of young people
starting a business of Meager et al (2004) which found no such effect. Again, there is a
significant gap in our knowledge and understanding about relative growth rates of ethnic
minority businesses compared to white owned businesses.
A survey of recent literature on small business performance has shown that human capital
is generally positively linked to success (Unger et al, 2011). Cowling (2006) divided
entrepreneurial human capital (EHC) into two categories: formal and informal. The former is
commonly proxied by the entrepreneur’s education level, and the latter usually by variables
such as the age, health, family, and prior experience.
In terms of formal human capital, there is fairly strong empirical support, across a number
of empirical studies, for the notion that businesses with more educated entrepreneurs
experience faster early stage growth (e.g. Cowling, 2002; Dimov and Shepherd, 2005; Rauch,
2005). Further, these studies also cover a reasonable time span, and different types of
businesses, which might suggest that we can generalise with more confidence about this
formal human capital effect.
However, empirical evidence on the impact of informal human capital is far less
conclusive (Cowling, 2006). This is probably due to the fragmented measures of informal
human capital used in the previous literature. For example, whilst there is virtually no
evidence found between performance and the age of entrepreneur, some studies have found a
positive relationship between experience and small business performance (Burke et al, 2000;
Honig, 1998; Watson et al, 2003; Westhead et al, 2003; Zarutskie, 2010).
Again our general review of the EHC literature shows that entrepreneurs’ characteristics
play a significant part in determining the rate of growth of firms. In a recessionary economic
environment we predict that these effects will become relatively more important in terms of
magnitude and their ability to distinguish between growing, stable and declining firms. This
might occur as external resources become scarcer during recessions so firms depend more on
the skills and capabilities of the entrepreneur to manage through recession.
H2a: Entrepreneurial human capital (education, experience, etc,) will have a positive impact
on the rate of small business growth.
H2b: The positive impact of EHC on the rate of small business growth will be magnified
during a period of economic recession.
2.3.
Growth orientations
The ambition to grow reflects the entrepreneur’s propensity towards innovation, risk taking
and strategic proactiveness, which are all essential elements of entrepreneurial orientation1
(Miller, 1983). Entrepreneurial orientation (EO) provides the firm with a basis for
entrepreneurial decisions and actions (Wiklund & Shepherd, 2003) and has been extensively
studied in the entrepreneurship literature. Miller (1983) argued that firms may benefit from
adopting an EO with uncertainties in the market, which require firms constantly seek out new
opportunities. This is especially relevant for smaller businesses given their obvious
competitive disadvantage against larger firms in terms of resources or network.
In an analysis of 51 empirical studies on EO, Rauch et al (2009) found a positive
correlation between EO and firm performance especially for micro businesses. Whilst most
1
We have to stress that our measure of growth orientation is just a very general indication of whether the
business aim to grow or not. Therefore, it may not be put into direct comparison with the more itemised and
systematic measures of EO in the previous literature.
studies on EO is focused on developed countries especially the US, the same relationship is
often not found in emerging economies (e.g., Tang et al., 2008; Wang, 2008). This leads to
the argument that the positive effect of EO is subject to constraints faced by firms operating
in different contexts (Lumpkin & Dess, 2001; Tang et al, 2008). For example, Zhao et al
(2011) considered organisational learning as a possible intervening variable between EO and
performance for a sample of Chinese enterprises and found that there is a learning process
before firms with EO start to grow.
Whilst achieved growth is, in part, a reflection of the entrepreneurs’ willingness to act on
opportunities identified, in a recessionary environment, when the flow of potential
opportunities diminishes, even entrepreneurs with a willingness to seek growth may be
constrained by a lack of feasible opportunities and resources. Thus we predict that the
generally positive effect of EO will be moderated during periods of economic recession.
H3a: There is a positive relationship between growth orientation and small business growth.
H3b: The positive growth orientation effect on small business growth will diminish during a
period of recession
2.4.
Access to finance
The availability of credit to entrepreneurs with good investment opportunities is one of the
key drivers of economic growth and competition (Beck & Demirguc-Kunt, 2006; Marlow &
Patton, 2005; Cassar, 2007). It is widely recognised that entrepreneurial activity, and the
growth of small businesses, can be are constrained by limited access to financial resources
arising from imperfections in capital market allocations (e.g., Cooper et al, 1994; Honig,
1998; Marlow & Patton, 2005; Revest and Sapio, 2010; Westhead & Storey, 1997).
Small firms’ access to finance is directly linked to capital structure and types of financing
used, which in turn are found to be associated with firm and entrepreneur characteristics
(Cassar, 2003 and 2004). Other studies also link financial capital to human capital. Chandler
and Hanks (1998) suggested that human and financial capital may be substitutes for each
other. Their analysis showed that firms with either high levels of founder human capital or
high levels of financial capital perform similarly with firms having high levels of both. On
the other hand, Brinckmann et al (2011) shifted their attention from supply side to demand
side and argued that the financial management competence of a firm’s founding team can
help overcome resource restrictions of new firms and foster their growth. Their empirical
results, however, are mixed. They found a more significant role of finance-seeking (external
and internal finance) skills than strategic financial management skills on new venture growth.
Whilst the availability of finance is generally important to support the development of new
investment opportunities at the firm level, we predict that during an economic recession with
credit rationing at the heart of it firms that are able to successfully secure finance will achieve
much greater relative growth than would be the case in a more stable economic environment.
This is, in part, because relatively few firms are able to secure finance, and hence only these
firms are able to take advantage of any remaining opportunities for growth.
H4a: Small business growth is positively associated with the availability of finance.
H4b: This positive availability of finance effect on small business growth will be magnified
during a period of recession.
2.5.
Macroeconomic conditions
Economic downturn and unfavourable financial market conditions will undoubtedly affect
the operation and survival of firms. Given the economic importance and vulnerability of
small businesses, a better understanding on how adverse macro economic conditions
influence entrepreneurial activities is crucial to effective crisis management by small
businesses (Herbane, 2010).
Several studies have indicated that the relationship between small business survival or
growth and its common determinants can be undermined during economic downturns (e.g.
Hilmersson, 2013) and since the outburst of the current financial crisis, there have been some
timely studies investigating the impact one of the severest recessions has on the SME sector
(e.g. Cowling et al., 2012; Smallbone et al., 2012a). Generally speaking, there are two
contradicting views, in the sense that recession either influences small business sector
negatively, or have no effect (a summary of recent studies on the recession-performance
relationship can be found in Table 1). With respect to the first view, it is argued that SMEs
are more vulnerable to economic downturns because their comparative disadvantage against
larger firms is likely to be exaggerated during a recession. Factors influencing SME
performance during a recession include access to resources especially availability of finance
(Cowling, et al., 2012), and bargaining power with external stakeholders such as suppliers
and customers. Empirical studies have found that during an economic recession, small
businesses are likely to perform less well and eventually, their chance of survival will be
reduced (Fotopoulos and Louri, 2000; Smallbone et al., 1999 and 2012a; Storey, 1994). The
rationale behind the ‘SME immune to economic downturn’ view is that SMEs are more
flexible in adjusting resource inputs, processes, prices and products (Reid, 2007) and
therefore more likely to pursue growth-oriented strategies (Latham, 2009). Moreover, it is
argued that the decision and outcome of growth for entrepreneurial firms could lie within the
entrepreneur level (Westhead and Wright, 2011; Wright, 2013) or even be modelled as a
random process (Coad, et al., 2013), which may be less affected by macroeconomic
conditions. For example, Requena and Silvente (2005) find that small- and medium-sized
enterprises (SMEs) base their export decisions on ‘typical’ export behaviour, which is not
affected by economic recessions.
Recent studies have shown that recessions are more likely to hit SMEs in certain sectors
(Bank of England, 2010; ONS, 2011) or with certain characteristics, whilst other SMEs are
more resilient (Kitching, et al., 2009 and 2011; Smallbone, et al., 2012). For example,
Kitching, et al. (2009) note that the current credit crunch affects UK small businesses in
various ways, and that "all small businesses necessarily suffer during periods of generalised
credit restrictions must be rejected". Grilli (2010) found that established start-up firms with
more experience entrepreneurs are actually less likely to survive during negative industrial
shocks. Grilli’s argument is that more experienced entrepreneurs have a wider range of career
options so may voluntarily exit the market during an industry crisis when the opportunities to
stay is too high. The bottom line, therefore, is there are certain kind of smaller businesses
more likely grow during adverse market shocks.
Insert Table 1 Here
3.
Method
This section describes the data source for this study and the survey method from which the
data is derived, followed by a discussion on both the dependent and independent variables in
the analysis.
3.1.
Sample
This study is intended to analyse existing data from two previous survey sources which
cover information of small businesses in the pre-recession and recessionary periods,
respectively.
The pre-recession data is derived from Annual Small Business Survey (ASBS) in 2007/08.
The ASBS survey has been conducted on an annual basis2 since 2003 and the 2007/08 survey
involved a large-scale telephone survey conducted by IFF Research Ltd between November
2007 and March 2008 to monitor key trends in the characteristics and perceptions of small
business owners and managers. The main purpose of the survey is to gauge the needs and
concerns of small businesses and identify the barriers that prevent them from fulfilling their
2
After 2008, the survey will be conducted biennially.
potential. A total of 9,362 SMEs (businesses with fewer than 250 employees) were
interviewed using a stratified random sample selection method evenly across thirteen regions
in the UK and the samples were randomly drawn across all commercial sectors of the
economy. Amongst the pre-recession sample SMEs, 45% are micro enterprises (0 to 9
employees), 38% are small enterprises (10 to 49 employees) and 17% are medium enterprises
(50 to 249 employees).
Conducted by the UK Department for Business, Innovation & Skills, a sample of the SMEs
entering the 2007/08 ASBS were re-contacted in a series of ‘Business Barometer’ surveys to
determine how well or badly they have performed in the previous year, and to assess their
levels of business confidence going forward. On average 500 SMEs were re-surveyed using
questions similar to the 2007/08 ASBS in each of the seven ‘Business Barometer waves’,
starting from December 2008 to February 2010 with intervals of two to three months. The
survey period coincides the latest financial crisis therefore gives us the opportunity to
investigate how business attitudes and access to finance by UK SME change pre- and postrecession. The ‘matching’ of the 2007/08 ASBS and ‘Business Barometer’ surveys yield a
dataset of 3,506 SMEs. The composition of within recession sample SMEs is fairly similar to
the pre-recession sample, with 44% being micro enterprises, 33% small enterprises and 23%
medium enterprises.
3.2.
Dependent variables
Two measures of performance are used in this study, namely percentage changes in
employment (EGROWTH) and sales (SGROWTH). In both surveys, questions were asked
explicitly on the firm’s current number of employees and turnover, as well as the
performance the year before. Pre-recession growth is calculated as the percentage change in
employment and sales between the 2007/08 ASBS and the previous year. Within-recession
growth is calculated as the percentage change in employment and sales between the
‘Business Barometer’ surveys and the 2007/08 ASBS. In both cases, the performance
variables are winsorised at 1% level to remove the effect of outliers.
As well as exploring recent actual performance, this study seeks to understand the future
growth aspirations of smaller firms going forward. To measure growth orientation, both sets
of surveys asked business owners whether or not they aimed to grow their firms over the next
two to three years. Accordingly, we define growth orientations (ORIENTATION) as a binary
variable that equals 1 if the answer to the above question is a ‘yes’ and 0 otherwise.
3.3.
Explanatory variables
Independent variables in this study can be classified into four groups: business
characteristics, owner/entrepreneur characteristics, access to finance and recessionary time
indicators.
The main business characteristics include firm size, age, sector, region, corporate structure,
sector and so on. Firm size is measured by employee numbers (EMP). Business age is
reported in the dataset as banded variables (up to 10 years, 11 to 20 years and more than 20
years, labelled as AGE_10LESS, AGE_11TO20 and AGE_20MORE, respectively). Variables
on corporate structure include whether or not a business is family owned (FAMOWN) or
incorporated (CORP).
Owner/entrepreneur characteristics measure the firm’s human capital and consist of owner
age (OAGE), gender (whether or not the business is women led, WLED), race (whether or not
the business is minority group led, MLED), prior experiences and level of education. An
experienced employer (EXP) is defined as having previously set up a business, charity or
been self-employed. The level of education (DEGREE) is measured by whether or not the
owner has a university degree or above.
Both the 2007/08 ASBS and the ‘Business Barometer’ asked whether a firm applied for
finance during the last 12 months and if so, the outcome of the application. Based on the
outcomes of financing applications, a firms is defined as ‘fully constrained’ if its application
was denied (NOACCESS) and as ‘partly constrained’ if it only obtained some but not all of
the finance required (PARTACCESS). The base category is firms either with no need for
external finance or those that have successfully obtained all the finance required
(FULLACCESS). Last, seven recessionary time indicators (WAVE1 to WAVE7) are defined to
match the timing of the seven ‘Business Barometer’ surveys covering the entire duration of
the economic recession.
3.4.
Empirical Methodologies
The primary objective of this study is to investigate the recessionary growth performance
of the small business sector, and the determinants of growth outcomes. Since both growth
measures (percentage change in employment and sales) are by construction continuous
variables, an OLS model specification allowing for clusters effect is used with adjustments
made for robustness of the standard errors. In this way, our analysis is able to capture the
possible unobservable group effects (e.g. within sectors) in our data set. Further, we would
also like to examine entrepreneurs’ growth intention going forward. In doing this, we use
probit regression models since the dependent variable is binary and coded 1 if the
entrepreneur has an explicit growth orientation for the future and 0 otherwise. The model
uses maximum likelihood estimations and the model chi-square and log likelihood are
reported to test the model’s goodness of fit.
4.
Results
This section first reports sample descriptive statistics for the variables and then the
empirical results from multivariate regression analysis.
4.1.
Descriptive statistics
Table 2 reports the descriptive statistics of dependent and independent variables. There are
3,067 firm-level observations for the analysis of small business performance during the
recession, whereas the sample size for pre-recession analysis from the 2007/08 ASBS is
6,5973. Since most of the variables are dummies variables, it is worth noting that the mean of
each dummy is equivalent to the percentage of observations where the variable takes a value
of one.
From the 2007/08 ASBS data, the average employment and sales growth are 2.6% and
5.2%, respectively. The average absolute change of sales is £87,050 and the average
employment change is 1.3. In addition, over 70% of smaller firms had an explicit growth
ambition. During the current recession, whilst employment has actually grown by a higher
rate (3.3%), the average turnover has decreased by almost 9%, which translates to an average
decrease in sales of £113,000. Figure 1 and Figure 2 illustrate how the proportions of
respondents reporting sales/employment increase and intention to grow in the future have
changed over time before, and during, the recession (between September 2008 and February
2010). It can be seen that both employment and sales performance is significantly lower
compared to pre-recession levels, whilst the growth ambition of small businesses has picked
up as the recession approached its end.
Insert Figure 1 Here
Insert Figure 2 Here
Table 1 also presents the univariate mean-comparison test results for firms before and
during the latest financial crisis. It is shown that, compared to pre-recession periods, firms
3
We also try to ‘match’ the pre- and within-recession samples and do the same analysis for the matched sample
as a robustness check. However, this does not alter our empirical findings significantly but has increase the
value of error terms due to the considerable decrease in sample size.
during the recession generally have lower growth ambitions, and are more likely to be
financially constrained.
Insert Table 2 Here
4.2.
Multivariate regression results
The starting point was to econometrically model the dynamics of business sales and
employment growth before and during the period of economic recession. As we are
particularly interested in how performance changes when the economy moves into recession,
we estimate separate pre-recession and within recession models.
Table 3 reports the coefficient estimates for both sales and employment growth equations.
The first two specifications show the pre-recession employment growth. It can be seen that
larger firms are more likely to experience employment growth ( = 0.15, p < 0.01) but the
negative coefficient on the quadratic term indicates that there is a diminishing effect on the
relationship between size and employment growth. Here, employment ceases to grow when
the firm has over 120 employees4, showing a diseconomy of scale. Younger firms or firms
with younger owners are more likely to have their employment number increased. We
include sales growth (employment growth in sales growth equation) as a control variable and
find it significantly and positively correlated with employment change ( = 0.44, p < 0.01).
Businesses that export their products and/or led by ethnic minority owners are more likely to
have experienced increased employment in non-recessionary times. We include further
controls for entrepreneurial growth orientation and access to finance in Model 2. On average,
growth-oriented businesses’ employment grow by 2.6% more than the rest of the firms, and
compared to those with full access to finance, businesses with partial or no access to finance
have suffered from lower employment growth rate by 8% and 5.8%, respectively.
4
The number is derived by calculating the turning point of the employment growth function, as the absolute
value of the ratio between the coefficient estimate of EMP, divided by 2 times the coefficient estimate of EMP2.
Employment growth during the recession shows some remarkable differences (Models 3
and 4). It is only business characteristics variables that are significant in explaining
employment growth during a recessionary period. Similar to non-recessionary period, larger
( = 0.20, p < 0.01) but younger firms with higher sales growth ( = 0.30, p < 0.01) exhibit
greater capabilities to weather economic downturns than the other firms. The employment
number of the whole small business sector seems not to be significantly affected by the
financial crisis: although firms were not able to grow their employment size for the whole
duration of the recession, there is no sign of decrease in employment, either. As predicted, the
coefficient estimate for growth orientation is insignificant. However, we also could not find
any significant evidence on the effect of financial constraints on employment growth.
Insert Table 3 Here
The rest of Table 3 reports the coefficient estimates for small business sales performance
before (Models 5 and 6) and during the recession (Models 7 and 8). Similarly, the prerecession growth in sales is positively related to the size of the firm and negatively related to
firm age. Business that export outside the UK outperform those do not export by at least 2.3%
in terms of sales growth. The growth ambition of firms has even a larger effect on sales than
employment growth. Compared to businesses with no growth ambitions, growth-oriented
businesses outperformed the other firms by over 5%. Again, it is found that financial
constraints reduce sales growth.
Similar to recessionary employment growth, larger but younger firms are more likely to
experience sales growth. Positive employment growth also tends to create a multiplier effect
on sales growth during the recession ( = 0.05, p < 0.01). There is still no significant impact
of human capital variables on recessionary sales growth. A clear time dynamic is identified,
where sales performance continued to deteriorate during the recession even towards the end
of the crisis. As shown in Model 8, growth orientation in a recessionary period has a positive
effect on sales growth ( = 3.04, p < 0.01). Moreover, businesses that failed to get any
funding from their lenders were associated with a decline in sales by 3.2%, although the
effect is even larger for those with only partial access to finance ( = 9.78, p < 0.01).
The final models estimate the probability that an entrepreneur will have a growth
orientation. For ease of interpretation we report the marginal effects which show the
probability that an entrepreneur or business with a specific characteristic will be more (or
less) likely to have a growth orientation. We use a common set of variables identified in the
previous growth models. The coefficient estimates for these growth orientation models are
reported in Table 4.
The UK small business sector has maintained it growth ambition during the recession and
SMEs’ intention to grow in the future is not hindered by the actual employment performance
of the firm, or even the shortage of financial resources. Moreover, there is a significant
‘feedback’ effect from sales growth to growth orientation ( = 0.001, p < 0.01). We find
considerable similarities regarding the types of entrepreneurs and smaller firms that are
growth-orientated before and during the recession. First, larger but younger firms are more
ambitious on future growth. Second, businesses structured as formal corporations and/or
involved in exporting are more likely to seek future growth while family-owned businesses
are less ambitious. Third, key indicators of entrepreneur ability, especially education, have a
positive and significant effect on businesses’ growth orientations. Finally, entrepreneurs’
personal characteristics have a less pronounced effect on growth ambitions except for the age
of the owner, which shows that younger entrepreneurs are more likely to seek future growth.
Regarding the dynamics of SME owners’ growth intention during the recession, growth
ambitions are more likely to be found at the start of the recession and as market conditions
worsened during the recession, entrepreneurs simply became more ‘realistic’ on future
growth until the crisis approached its end.
Insert Table 4 Here
5.
Discussion
The summary results are presented in Table 5. First, we find business characteristics
important in predicting pre-recession SME growth performance (e.g. size and age) also
important determinants of within recession performance. Moreover, consistent with
hypothesis H1, additional predictors of within recession performance have been discovered,
such as sector effects. Second, contrary to business characteristics, EHC variables have little
prediction power for both employment and sales growth during the recession thus hypothesis
H2 is not supported. Third, we find partial support for hypothesis H3 in the sense that the
positive effect of entrepreneurial growth orientation on growth disappears when looking at
the employment performance during the recession. Similarly, hypothesis H4 is also only
partially supported as better access to finance is crucial to achieving recessionary growth in
sales but not employment.
In terms of the question as to how many firms are still capable of achieving growth during
the recession, we note that between 20 and 30% of firms grew their sales which is much less
than the 50% that grew in more favourable economic conditions prior to the recession.
Equally, between 15 and 20% of firms grew their employment during the recession, but again
this is lower than in the pre-recession period when 30% grew their employment. This
suggests that the recession had a very strong adverse effect, at least in the first six months, on
the ability of firms to grow. From this, and explicitly focusing on our second question
concerning employment growth, we note that more firms were creating jobs as the recession
continued, even when fewer firms were managing to grow their sales. This might suggest that
after an initial downward employment correction as the recession unfolded, in general the
small business sector was able to recover quite quickly as more and more firms were willing
to hire employees. To this end, it could be argued that SMEs are indeed more resilient and
more capable of creating jobs as the economy slowly moves out of recession.
In terms of what types of firms and entrepreneurs were more likely to grow during a
recession, we note that firms in all manufacturing sectors experienced significant declines in
sales, and firms in construction were most likely to experience declines in employment.
Taken together these results show that industry sector is an important determinant of growth
outcomes during a recession. And the importance of this feature of economic growth is
heightened by the fact that in periods of economic growth industry sector plays a very minor
role in the determination of employment and sales growth which is fairly randomly
distributed across all industry sectors.
Other firm characteristics were also found to be important in determining growth. Taken
together, our results show that business characteristics are important determinants of growth
in both recessionary and nonrecessionary period but this is not the case for entrepreneurial
human capital (EHC). During the recession, it is the access to financial resources rather than
the more subjective measures of human capital that are more important determinants of
recessionary growth, especially regarding sales. This suggests that in more stable economic
environments many more firms are able to take advantage of general growth in demand
without having to compete vigorously with other firms and entrepreneurs. Nevertheless,
during a recession when the whole small business sector is further constrained by limited
resource, only the entrepreneurs that have access to essential financial resources can manage
to achieve growth. Yet it is also the case that willingness to seek growth was found to be a
positive attribute in more stable economic conditions, but this did not hold in a recessionary
environment. This might suggest that exogenous forces, declining demand, reduced
investment activity, overwhelm these generally positive effects.
As this global economic recession has its roots in the financial sector, our findings show
that credit constraints at the firm level will inhibit a firm’s ability to grow their sales, thus
create a negative multiplier effect. The effect of capital availability on business performance
especially sales is consistent with the traditional view that entrepreneurial activity, and the
growth of small businesses, can be seriously constrained by limited access to financial
resources (Auerswald and Branscomb, 2003; Revest and Sapio, 2010). However, this could
also mean that firms refused any finance by lenders are indeed of poorer quality and less
creditworthy (Nightingale et al., 2009), leading to their inferior performance. Importantly, the
results show that credit rationing in recessions leads to a more substantial reduction in growth
performance than was the case in the pre-recessionary period. This suggests that capital
constraints magnify performance differences between firms and can lead to lower growth
rates in the small business sector than would have been achieved otherwise. Generally, firms
are not able to increase their sales during the recession especially during the later periods of
the recession, when average firm sales decreased by £60,000 to £200,000. This has
significant potential policy implications for governments seeking to promote growth and job
creation in the smaller firm sector of the economy.
Further, we note that under any economic conditions there is a positive synergy between
sales and employment growth. This positive relationship is only slightly diminished in terms
of its effect size during recessions. What this does suggest is that any policy levers that
stimulate either job growth or sales growth will be more likely to create a positive economic
multiplier.
In relation to our fourth, and final, question relating to future growth orientations, we have
several important insights. Firstly, general growth orientations do decline during a recession,
with 10% fewer firms reporting these intentions, but this depressing effect begins to recover
within six months of the onset of the recession. In terms of which types of firms and
entrepreneurs have growth orientations, we find that firm size has a positive effect, and firm
age a negative effect. Family businesses were less likely to be growth orientated, but
exporting firms more likely to be. In relation to entrepreneur effects, we find that younger
entrepreneurs and those with a university education are more likely to be growth orientated
during a recession. Taken together these findings suggest that certain types of entrepreneurs
and firms tend to view recession as times to scale down their activities and try and weather
the economic storm, whereas others see recessions as opportunities to gear up their firms for
future growth.
Insert Table 5 Here
6. Conclusion
To summarise our overall findings, we are drawn to the conclusion that recessions do take
their toll on the smaller business sector, but these effects appear relatively short lived in
general, and affect specific types of small businesses and entrepreneurs more than others. But
perhaps our most significant finding is that in a stable, and growing macroeconomic
environment growth in more randomly spread across all types of firms and entrepreneurs.
This is not true in periods of economic downturns when only the best entrepreneurs, in terms
of larger size and better access to finance, are able to grow their businesses.
For policy-makers our results suggest that helping firms’ access finance may create a
positive growth multiplier, and many countries have adopted this policy position. But more
importantly, any policy levers that stimulate jobs or general spending in the economy will
help create a positive jobs-growth multiplier as they tend to operate in parallel in smaller
firms. As to the general capability of the small business sector to grow and help drag
depressed economies forward, our findings do offer some support for the contention that
SMEs are more resilient and flexible to cope with the disequilibrium caused by economic
recessions.
In terms of potentially interesting avenues of future research, it would be helpful to
establish the timescale over which any policy interventions in the small business sector take
to manifest themselves in measurably better growth outcomes. Equally, the question of how
long it takes for growth orientations to translate themselves into actual growth is important.
Moreover, since there is clear evidence of a ‘feedback’ effect from actual growth (in sales) to
growth orientation, it is worth of further investigation of the inter-relationship between
growth performance and growth orientation in a longitudinal context. Finally, having broadly
established that entrepreneurial quality is a fundamental determinant of growth during
recessions, the question of how strong an economy has to be before good and bad
entrepreneurs are capable of surviving and growing is hugely interesting and important.
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Table 1
Summary of Recent Studies on the Performance-Recession Relationship
Study
Sample used
Main conclusion(s)
Bank of England (2010); ONS
(2011)
Coad, et al. (2013)
Grilli (2010)
UK private business: 2008 –
2010
UK (private bank data): 2004
– 2010
Italian start-ups: 1995 – 2000
Hilmersson (2013)
Swedish SMEs: 2007 – 2011
Kitching, et al. (2009 and
2011); Smallbone, et al.
(2012b)
Smallbone et al. (2012a)
UK small businesses: 2009 –
2011
Construction sector more depressed than other sectors,
such as business services.
Firm growth follows random patterns. Growth paths
influence survival.
Established start-ups with more experience
entrepreneurs less likely to survive during negative
industrial shocks
The scope and speed of internationalisation has a
positive performance effect during market turbulence,
but not the scale.
SME performance varies within- and post-recession,
dependent on firms’ adaptations to the recession.
Wright (2012)
Theoretical
UK and New Zealand SMEs:
2008 – 2009
Recession has no constantly negative effect on firm
survival. Businesses shown performance resilience to
the recession varies w.r.t. firm characteristics.
Entrepreneurial cognition helps to shape the patterns
and types of growth. Entrepreneurs’ role in shaping
growth should be better understood, besides the
commonly considered factors such as access to finance.
Table 2
Variable Definition and Sample Descriptive Statistics
(1) Pre-recession (N = 6,597)
Variable
Definition
Mean Std. Dev.
Min
Max
Dependent Variables
EGROWTH
% Change in employee numbers over the past 12 months
2.55
29.67
-100.00
150.00
SGROWTH
% Change in sales over the past 12 months
5.24
18.77
-50.00
100.00
ORIENTATION
Firm aiming to grow in the next 2-3 years (0, 1)
0.73
0.45
0.00
1.00
Independent Variables
Business characteristics
EMP
Number of employees
25.58
37.57
0.00
249.00
AGE_10LESS
Firm less than 10 years old (0, 1)
0.04
0.42
0.00
1.00
AGE_11TO20
Firm between 11 and 20 years old (0, 1)
0.21
0.41
0.00
1.00
AGE_20MORE
Firm more than 20 years old (0, 1)
0.75
0.43
0.00
1.00
CORP
Firm is incorporated (0, 1)
0.81
0.39
0.00
1.00
FAMOWN
Firm is family owned (0, 1)
0.64
0.48
0.00
1.00
EXPORTER
Firm exports (0, 1)
0.26
0.45
0.00
1.00
Primary Industries
Sector dummy (0,1)
0.02
0.16
0.00
1.00
Metals Manufacturing
Sector dummy (0,1)
0.10
0.29
0.00
1.00
Non-metals Manufacturing
Sector dummy (0,1)
0.04
0.19
0.00
1.00
Other Manufacturing
Sector dummy (0,1)
0.10
0.30
0.00
1.00
Construction
Sector dummy (0,1)
0.33
0.47
0.00
1.00
Retail & Wholesale
Sector dummy (0,1)
0.07
0.25
0.00
1.00
Transport &Communication Sector dummy (0,1)
0.21
0.41
0.00
1.00
Business Services
Sector dummy (0,1)
0.04
0.19
0.00
1.00
Other services
Sector dummy (0,1)
0.07
0.25
0.00
1.00
Owner/Entrepreneur characteristics
OAGE
Owner’s age
50.39
10.41
19.00
88.00
WLED
Women-led business (0, 1)
0.60
0.86
0.00
3.00
MLED
Ethnic minority-led business (0, 1)
0.57
0.88
0.00
3.00
EXP
Employer with prior experience (0, 1)
0.04
0.18
0.00
1.00
DEGREE
Employer with college degree or above (0, 1)
0.41
0.49
0.00
1.00
Access to Finance
FULLACCESS
Poor firm-bank relationship (0, 1)
0.96
0.22
0.00
1.00
NOACCESS
Firm-bank relationship neither good or poor (0, 1)
0.01
0.08
0.00
1.00
PARTACCESS
Good firm-bank relationship (0, 1)
0.02
0.13
0.00
1.00
Recessionary time indicators
WAVE1 – WAVE7
Dec-08, Feb-09, April-09, Jun-09, Sep-09, Dec-09, Feb-10 Barometer Survey firms (0, 1), respectively
* p .10; ** p .05; *** p .01 for univariate comparison test of difference in means.
(2) Within-recession (N = 3,067)
Mean
Std. Dev.
Min
(1) vs. (2)
Max
Mean
3.34
-8.86
0.71
60.66
25.58
0.45
-95.56
-100.00
0.00
336.36
33.33
1.00
***
***
*
31.99
0.07
0.39
0.54
0.87
0.62
0.29
0.03
0.15
0.05
0.13
0.25
0.06
0.20
0.05
0.05
44.38
0.27
0.49
0.50
0.34
0.47
0.45
0.18
0.36
0.23
0.34
0.43
0.24
0.40
0.21
0.21
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
240.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
***
***
***
51.55
0.10
0.04
0.18
0.45
9.74
0.29
0.20
0.39
0.50
21.00
0.00
0.00
0.00
0.00
87.50
1.00
1.00
1.00
1.00
0.92
0.07
0.01
0.28
0.26
0.10
0.00
0.00
0.00
1.00
1.00
1.00
***
***
**
***
*
*
**
***
*
Table 3
OLS Regressions: Pre- and Within-recession Employment and Sales Growth
Employment Growth
Pre-recession
Within-recession
EMP
EMP 2
AGE_11TO20
AGE_20MORE
SGROWTH
Model 1
0.150***
(0.025)
-0.001***
(0.000)
-4.119**
(1.978)
-8.346***
(1.911)
0.444***
(0.019)
Model 2
0.141***
(0.025)
-0.001***
(0.000)
-3.942**
(1.977)
-8.096***
(1.912)
0.432***
(0.019)
-0.473
(0.948)
0.312
(0.774)
1.732**
(0.823)
-1.332
(3.224)
-0.678
(2.595)
1.285
(2.900)
0.536
(2.595)
0.172
(2.428)
3.274
(2.697)
2.067
(2.501)
1.313
(2.979)
-1.665
(2.750)
-0.164***
(0.035)
-0.021
(0.919)
1.489*
(0.904)
-1.472
(1.973)
-0.894
(0.746)
-0.670
(0.949)
0.385
(0.774)
1.562*
(0.826)
-1.695
(3.223)
-0.779
(2.594)
1.069
(2.897)
0.512
(2.593)
0.100
(2.426)
3.163
(2.695)
1.893
(2.500)
1.588
(2.980)
-1.585
(2.748)
-0.147***
(0.035)
-0.032
(0.918)
1.493*
(0.903)
-1.521
(1.971)
-0.935
(0.746)
2.575***
(0.831)
-7.964*
(4.575)
-5.759**
(2.591)
Sales Growth
Within-recession
Model 3
0.203***
(0.026)
Model 4
0.201***
(0.026)
Model 5
0.030***
(0.006)
Model 6
0.021***
(0.006)
-8.351*
(4.366)
-14.847***
(4.374)
0.300***
(0.043)
-8.399*
(4.370)
-14.981***
(4.391)
0.295***
(0.043)
-4.852***
(1.240)
-8.648***
(1.193)
-4.394***
(1.229)
-7.962***
(1.184)
0.175***
(0.007)
2.302***
(0.585)
0.159
(0.485)
2.786***
(0.515)
-3.186
(2.020)
-0.805
(1.627)
-1.960
(1.817)
-0.730
(1.628)
-2.727*
(1.522)
-2.644
(1.691)
-1.462
(1.569)
-4.704**
(1.867)
-2.880*
(1.724)
-0.094***
(0.022)
0.502
(0.576)
-0.221
(0.567)
-0.252
(1.238)
1.210***
(0.467)
0.167***
(0.007)
1.775***
(0.582)
0.360
(0.482)
2.305***
(0.513)
-3.917*
(2.003)
-1.009
(1.613)
-2.364
(1.801)
-0.672
(1.613)
-2.820*
(1.509)
-2.755
(1.676)
-1.773
(1.555)
-4.542**
(1.852)
-2.746
(1.709)
-0.058***
(0.022)
0.447
(0.571)
-0.206
(0.562)
-0.442
(1.227)
1.032**
(0.464)
5.302***
(0.512)
-5.373*
(2.847)
-6.844***
(1.611)
EGROWTH
CORP
Pre-recession
-7.482**
(3.350)
3.612
(2.414)
-0.934
(2.640)
-11.459
(9.124)
-15.289**
(7.411)
-8.947
(8.209)
-11.865
(7.444)
-14.000*
(7.155)
-5.888
(8.095)
-11.798
(7.343)
-9.039
(8.670)
-1.886
(8.606)
-0.089
(0.115)
0.710
(3.741)
2.653
(5.553)
1.704
(5.662)
-2.263
(2.303)
-7.693**
(3.355)
FAMOWN
3.899
(2.424)
EXPORTER
-1.156
(2.661)
Primary
-11.155
Industries
(9.131)
Metals
-15.041**
Manufacturing
(7.415)
Non-metals
-8.689
Manufacturing
(8.215)
Other
-11.620
Manufacturing
(7.451)
Construction
-13.695*
(7.161)
Retail &
-5.638
Wholesale
(8.099)
Transport
-11.790
&Communication
(7.347)
Business Services
-8.780
(8.673)
Other Services
-1.459
(8.612)
OAGE
-0.078
(0.115)
WLED
0.726
(3.742)
MLED
2.695
(5.555)
EXP
1.463
(5.665)
DEGREE
-2.312
(2.306)
ORIENTATION
2.240
(2.506)
PARTACCESS
-8.645
(10.473)
NOACCESS
-4.788
(4.155)
WAVE2
0.351
0.358
(4.011)
(4.021)
WAVE3
-1.513
-1.676
(4.025)
(4.030)
WAVE4
0.072
-0.044
(4.034)
(4.038)
WAVE5
2.553
2.445
(4.049)
(4.055)
WAVE6
3.596
3.307
(4.041)
(4.046)
WAVE7
5.140
5.061
(6.632)
(6.636)
N
6597
6597
3067
3067
6597
6597
Adjusted R2
0.112
0.113
0.041
0.041
0.125
0.142
F statistics
25.358***
23.812***
4.523***
4.251***
29.653***
31.211***
* p .10; ** p .05; *** p .01. Asymptotic robust standard errors reported in the parentheses.
Model 7
0.084***
(0.028)
-0.000
(0.000)
-4.532**
(1.824)
-3.103*
(1.835)
Model 8
0.076***
(0.028)
-0.000
(0.000)
-4.505**
(1.822)
-3.056*
(1.838)
0.053***
(0.008)
-0.616
(1.411)
-1.249
(1.011)
5.434***
(1.100)
-6.972*
(3.814)
-13.632***
(3.091)
-11.503***
(3.425)
-9.039***
(3.109)
-3.682
(2.992)
-7.359**
(3.382)
-9.168***
(3.066)
0.176
(3.627)
-1.228
(3.596)
-0.007
(0.048)
-0.897
(1.565)
-1.204
(2.320)
0.863
(2.368)
-0.573
(0.964)
0.052***
(0.008)
-0.860
(1.410)
-0.954
(1.013)
5.067***
(1.107)
-6.764*
(3.808)
-13.392***
(3.086)
-11.255***
(3.420)
-8.722***
(3.105)
-3.448
(2.988)
-7.148**
(3.376)
-9.194***
(3.061)
0.498
(3.620)
-0.807
(3.591)
0.008
(0.048)
-0.866
(1.562)
-1.181
(2.316)
0.621
(2.363)
-0.656
(0.963)
3.037***
(1.046)
-9.775**
(4.363)
-3.184*
(1.732)
-0.770
(1.676)
-2.793*
(1.680)
-2.744
(1.684)
-4.783***
(1.688)
-2.946*
(1.687)
-5.594**
(2.766)
3067
0.062
5.960***
-0.648
(1.676)
-2.553
(1.682)
-2.569
(1.686)
-4.605***
(1.690)
-2.627
(1.688)
-5.454**
(2.771)
3067
0.058
5.976***
Table 4
Probit Regressions: Pre- and Within-recession Growth Orientations
Pre-recession
Model 1
Business characteristics
EMP
AGE_11TO20
AGE_20MORE
EGROWTH
SGROWTH
CORP
FAMOWN
EXPORTER
Owner characteristics
OAGE
WLED
MLED
EXP
DEGREE
Within-recession
Model 2
Model 4
0.002***
(0.000)
-0.094**
(0.038)
-0.128***
(0.028)
0.001***
(0.000)
0.004***
(0.000)
0.080***
(0.015)
-0.045***
(0.012)
0.108***
(0.012)
0.002***
(0.000)
-0.092**
(0.038)
-0.126***
(0.028)
0.001***
(0.000)
0.004***
(0.000)
0.080***
(0.015)
-0.044***
(0.012)
0.107***
(0.012)
0.001***
(0.000)
-0.052
(0.036)
-0.115***
(0.034)
0.000
(0.000)
0.001***
(0.000)
0.061**
(0.026)
-0.074***
(0.018)
0.139***
(0.018)
0.001***
(0.000)
-0.051
(0.036)
-0.112***
(0.034)
0.000
(0.000)
0.001***
(0.000)
0.061**
(0.026)
-0.075***
(0.018)
0.138***
(0.018)
-0.007***
(0.001)
0.011
(0.014)
0.002
(0.014)
0.047
(0.030)
0.036***
(0.012)
-0.007***
(0.001)
0.011
(0.014)
0.002
(0.014)
0.046
(0.030)
0.035***
(0.012)
-0.004***
(0.001)
-0.022
(0.028)
0.027
(0.041)
0.060
(0.042)
0.044**
(0.018)
-0.004***
(0.001)
-0.022
(0.028)
0.026
(0.041)
0.062
(0.042)
0.044**
(0.018)
Access to finance
NOACCESS
0.098*
(0.059)
0.082**
(0.034)
PARTACCESS
Recessionary time indicator
WAVE2
WAVE3
WAVE4
WAVE5
WAVE6
WAVE7
N
Pseudo R2
χ2
Log likelihood
Model 3
6597
0.129
997.336
-3367.664
6597
0.130
1004.022
-3364.321
0.022
(0.077)
0.046
(0.030)
0.082***
(0.026)
0.067**
(0.027)
0.069**
(0.027)
0.078***
(0.027)
0.075***
(0.027)
0.072
(0.047)
3067
0.098
361.466
-1663.886
0.080***
(0.026)
0.067**
(0.027)
0.068**
(0.027)
0.077***
(0.027)
0.075***
(0.027)
0.071
(0.047)
3067
0.099
363.660
-1662.789
* p .10; ** p .05; *** p .01. Marginal effects of the coefficient estimates are reported. Asymptotic robust standard errors are reported
in the parentheses.
34
Table 5
Summary of Hypotheses and Empirical Results
Hypotheses
Prediction
Result
H1: Business Characteristics
More important during recession
Yes
H2: Entrepreneurs Human Capital More important during recession
No
H3: Entrepreneurial Orientation
Less important during recession
Yes (partially)
H4: Access to Finance
More important during recession
Yes (partially)
35
Figure 1
Proportion of business with increased sales and employment before and during the recession
Employment change
Sales change
*Base: All SME employers (weighted data); unweighted N = 2,396 (pre-recession N = 2,138).
Figure 2
Proportion of business with a growth orientation before and during the recession
With growth orientation
No growth orientation
*Base: All SME employers (weighted data); unweighted N = 2,396 (pre-recession N = 2,138).
36